Search Results

Abstract

Objetivos: O objetivo deste estudo é comparar as curvas de aprendizagem dos especialistas em dois campos diferentes sem experiência prévia de dacriocistorrinostomia endonasal endoscópica e revelar as complicações com as taxas de sucesso cirúrgico.
Métodos: Foram investigados retrospectivamente 90 pacientes que receberam dacriocistorrinostomia endonasal endoscópica consecutiva com preservação da mucosa realizada por um oftalmologista (Grupo 1, n=45) e realizada por um otorrinolaringologista (Grupo 2, n=45) entre outubro de 2017 e outubro de 2019. Foram incluídos no estudo pacientes admitidos com epífora e diagnosticados com obstrução primária do ducto nasolacrimal adquirido como resultado do teste de irrigação lacrimal, com idade superior a 18 anos e com, pelo menos, 6 meses de acompanhamento. Em todos os casos, patologias adicionais, como o desvio do septo, foram avaliadas por meio da realização de imagens maxilofaciais. Os prontuários dos pacientes foram avaliados quanto à duração da cirurgia, complicações e desempenho funcional.
Resultados: A média de duração cirúrgica dos pacientes no Grupo-2 foi de 36,27 ± 11,61 minutos, enquanto no Grupo-1 foi de 43,62 ± 16,89 minutos, sendo a diferença estatisticamente significativa (p=0,018). O desempenho funcional no Grupo 1 foi de 84,4% (73,3% nos primeiros 15 casos, 93,3% nos últimos 15 casos) no Grupo 2, essa taxa foi de 88,9% (80% nos primeiros 15 casos, 93,3% nos últimos 15 casos) e a diferença não foi estatisticamente significativa (p=0,53). A intervenção do septo além da cirurgia endoscópica em ambos os grupos (p=0,03, p=0,005, respectivamente) e sangramento intenso durante a cirurgia (para ambos os grupos, p<0,0001) diminuiu significativamente o sucesso funcional.
Conclusão: A dacriocistorrinostomia endonasal endoscópica, realizada após o treinamento necessário, pode ser realizada com alto sucesso e com baixas taxas de complicações por oftalmologistas que não estão familiarizados com a cirurgia endoscópica após adquirirem experiência com trinta casos.

Keywords: Obstrução dos ductos lacrimais; Ducto nasolacrimal/cirurgia; Dacriocistorinostomia/métodos; Endoscopia; Oftalmologia/educação

Abstract

Objetivo: Desenvolver um aplicativo (TopEye) na plataforma iOS para dispositivos móveis que possibilite a captação e interpretação do mapa de cores gerados por qualquer topógrafo corneano através da inteligência artificial (IA).
Métodos: A execução, acompanhamento e avaliação do projeto foi utilizada a metodologia Scrum, processo de desenvolvimento interativo e incremental para gerenciamento de projetos e desenvolvimento ágil de software. O banco de padrões de diagnóstico gerado consiste em 1172 exemplos, divididos em: 275 padrões esféricos, 302 regulares simétricos, 295 regulares assimétricos e 300 irregulares (ceratocone). Para o desenvolvimento da inteligência artificial do aplicativo, foi estabelecido o treinamento da rede com 240 imagens de cada tipo de padrão, totalizando 960 (81,91%) padrões. O restante das imagens, 212 (18,09%), foram utilizadas para testar o aplicativo e usadas para gerar os resultados. O processo é semiautomático, assim a captação da imagem topográfica é realizada com smartphone, o examinador realiza o contorno do relevo corneano manualmente para em seguida a rede neural realizar o diagnóstico.
Resultados: O aplicativo diagnosticou 201 (94,81%) imagens corretamente. De um total de 212 imagens, o algoritmo errou a classificação de apenas 11 (5,19%). A principal ocorrência de erro foi na distinção das classes simétrica e assimétrica. No rastreio do ceratocone o aplicativo alcançou 95,00% de sensibilidade e 98,68% especificidade.
Conclusão: O trabalho resultou na obtenção de um aplicativo eficiente na captura da imagem topográfica pela câmera do smartphone e na interpretação da mesma através da inteligência artificial aplicada.

Keywords: Dispositivos móveis; Inteligência artificial; Topografia corneana; Astigmatismo

Abstract

PURPOSE: To evaluate the academic and professional trajectories of graduates from the Graduate Program in Ophthalmology and Visual Sciences at the Escola Paulista de Medicina, Universidade Federal de São Paulo, including geographic distribution, occupational characteristics, and scientific productivity.
METHODS: This descriptive, retrospective, quantitative study included 498 alumni who completed the program between 1979 and 2021. Data were obtained from institutional records and supplemented by public databases (Google Scholar, Lattes Platform, and LinkedIn). The analyzed variables included demographic characteristics, academic background, current professional role, and bibliometric indicators (citation count and h-index). Statistical analyses comprised nonparametric tests and linear regression, with a significance level set at 5%.
RESULTS: Most alumni were Brazilian (96.6%) and physicians (90.7%), predominantly located in the Southeast region (66.9%). Doctoral training was completed by 80.5% of participants. Alumni with current institutional ties to Universidade Federal de São Paulo or Hospital São Paulo demonstrated significantly higher citation counts and h-index values. No significant correlation was observed between time since graduation and citation count (p=0.185). Alumni engaged in academic roles or with postdoctoral training showed greater scientific productivity.
CONCLUSIONS: The findings highlight the strong academic performance and professional integration of alumni from Universidade Federal de São Paulo, particularly within public institutions and the Southeast region of Brazil. Doctoral training and institutional affiliation were associated with higher scientific productivity. Alumni tracking provides valuable insights into the impact of postgraduate programs and informs strategic planning and development.

Keywords: Factual databases; Program evaluation; Medical education; Graduate education; Ophthalmology; Linear models

Abstract

PURPOSE: This study aimed to examine factors related to the professionalism of ophthalmology residents.
METHODS: A cross-sectional study was carried out involving 48 ophthalmology residents in Brazil. Professionalism was assessed using the professionalism mini-evaluation exercise, completed by both preceptors and residents, and the Pennsylvania State College of Medicine Professionalism Questionnaire, completed by the residents. The association between the professionalism score assigned by the preceptor through the professionalism mini-evaluation exercise and various sociodemographic and educational variables was assessed. The correlation between the residents’ self-assessment across both instruments and the preceptor’s assessments was measured using Spearman’s Rho.
RESULTS: All 48 residents were included, with equal representation across the 3 years of residency. The majority were female (58.3%) and between 25 and 29 years old (66.7%). The average professionalism score on the professionalism mini-evaluation exercise given by the preceptors was 3.0 (75%). A significant association was found between the year of training and the score in the doctor-patient relationship domain, with first-year residents showing lower scores (p=0.002). Male residents had higher scores in the “Interprofessional” domain (p=0.031). Graduates from private medical schools scored higher in both the “doctor-patient relationship” (p=0.015) and “reflective skills” (p=0.033) domains. Lower interest in professionalism was linked to lower scores in the “Interprofessional relationships” (p=0.033) and “time management” (p=0.003) domains. A strong correlation was observed between preceptor’s professionalism mini-evaluation exercise scores and residents’ self-assessed professionalism mini-evaluation exercise scores (r=0.917). However, the correlation between the self-assessed professionalism mini-evaluation exercise and the Pennsylvania questionnaire scores was weak (r=0.226).
CONCLUSION: Professionalism scores among ophthalmology residents were associated with year of training, gender, type of undergraduate education, and level of interest in the topic.

Keywords: Internship and residency; Ophthalmology; Professional competence; Education, professional; Physician-patient relations; Surveys and questionnaires

Abstract

PURPOSE: We developed an artificial intelligence program for calculating intraocular lenses and analyzed its accuracy rate via ultrasonic biometry. This endeavor is aimed at enhancing precision and efficacy in the selection of intraocular lenses, particularly in cases where optical biometry is unavailable.
METHODS: Data was collected from the Hospital de Clínicas de Porto Alegre, which included cases of phacoemulsification with intraocular lens implantation, in which the lens selection was based on ultrasonic biometry. The program, implemented in Python, Java, and PHP, employs the ridge regression method. Two design options were developed: a basic model, which uses only keratometry variables (K1 and K2), axial size and final target refraction in the spherical equivalent, and an advanced model, which incorporates preoperative refraction and the patient's age. The Universal Barrett II formula was used to compare both models.
RESULTS: The sample consisted of 486 eyes from 313 patients, with 350 eyes used for program training and 136 for program validation. The spherical equivalent hit rates, with a variation of ±0.5 D, were 86% and 87.5% for the basic and advanced models, respectively, with no statistically significant difference between them. With the Barret Universal II formula, the success rate was 69%, which was significantly different from the values of the two aforementioned models (p<0.0001). The system was better for medium and long eyes but worse for short eyes (<=22.00 mm).
CONCLUSION: The developed artificial intelligence program was superior to the Barrett formula in terms of performance, in the general context and within the subgroup of patients with longer eyes. This innovation can considerably contribute to the selection of intraocular lenses, particularly in cases where optical biometry is unavailable.

Keywords: Biometry; Intraocular lens; Cataract; Artificial intelligence

Abstract

PURPOSE: This pilot study evaluated the diagnostic accuracy of a deep learning model for detecting pterygium in anterior segment photographs taken using smartphones in the Brazilian Amazon. The model’s performance was benchmarked against assessments made by experienced ophthalmologists, considered the clinical gold standard.
METHODS: In this cross-sectional study, 38 participants (76 eyes) from Barcelos, Brazil, were enrolled. Trained nonmedical health workers captured high-resolution anterior segment images using smartphones. These images were analyzed using a deep learning model based on the MobileNet-V2 convolutional neural network. Diagnostic metrics–including sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve–were calculated and compared with the ophthalmologists’ evaluations.
RESULTS: The deep learning model achieved a sensitivity of 91.43%, specificity of 90.24%, positive predictive value of 88.46%, negative predictive value of 92.79%, and an area under the curve of 0.91. Logistic regression revealed no statistically significant association between pterygium and demographic variables such as age or gender.
CONCLUSIONS: The deep learning model demonstrated high diagnostic performance in identifying pterygium in a remote Amazonian population. These preliminary findings support the potential use of artificial intelligence–based tools to facilitate early detection and screening in underserved regions, thereby enhancing access to ophthalmic care.

Keywords: Pterygium/diagnostic imaging; Smartphone; Diagnostic techniques, ophthalmological; Deep learning; Telemedicine; Artificial intelligence; Cross-sectional studies; Brazil/epidemiology

Abstract

PURPOSE: In Brazil, it has traditionally been standard practice to teach a wide range of surgical techniques to all ophthalmology residents, with the aim of equipping them to manage most ocular conditions. However, with modern developments, access to subspecialists has expanded to nearly the entire country. This raises the question of whether it is still necessary to teach numerous surgical techniques to every resident. This study evaluates the effectiveness of surgical training in Brazilian ophthalmology residency programs to determine if comprehensive surgical training for all residents is truly effective, thereby providing evidence to inform educational policy decisions.
METHODS: A cross-sectional study using a questionnaire distributed to physicians engaged in eye care.
RESULTS: A total of 137 physicians responded to the survey, with 104 (76.0%) having already completed their specialization. The findings indicate that most practicing ophthalmologists received surgical training during residency in cataract, glaucoma, oculoplastic, and strabismus surgeries. Nonetheless, many of these specialists no longer perform most of these surgeries in practice, except for cataract surgery. While 53.8% of those who completed residency reported satisfaction with their training, 35.6% indicated that they wished they had received better surgical preparation.
CONCLUSION: The training of ophthalmology specialists must be made more efficient. Training efficiency is reduced when time and resources are devoted to surgical procedures that many specialists will not perform in their careers.

Keywords: Opthalmologists; Teaching; Education, medical; Ophthalmological surgical procedures; Simulation training; Wet lab; Surveys and questionnaires

Abstract

PURPOSE: Diabetic retinopathy screening in low- and middle-income countries is limited by restricted access to specialized care. Portable retinal cameras offer a practical alternative; however, image quality – affected by mydriasis – directly influences the performance of artificial intelligence models. This study evaluated the effect of mydriasis on image gradability and AI-based diabetic retinopathy detection in real-world, resource-limited settings.
METHODS: The proportions of gradable images were compared between mydriatic and non-mydriatic groups. Generalized estimating equations were used to identify factors associated with image gradability, including age, sex, race, diabetes duration, and systemic hypertension. A ResNet-200d model was trained on the mobile Brazilian Ophthalmological dataset and externally validated on both mydriatic and non-mydriatic images. Model performance was evaluated using accuracy, F1 score, area under the curve, and confusion matrix metrics. Sensitivity differences were assessed using the McNemar test, and area under the curves were compared using DeLong's test. The Youden index was used to determine optimal classification thresholds. Agreement between macula- and disc-centered images was analyzed using Cohen's κ.
RESULTS: The mydriatic group demonstrated a higher proportion of gradable images compared with the non-mydriatic group (82.1% vs. 55.6%; p<0.001). In non-mydriatic images, lower gradability was associated with systemic hypertension, older age, male sex, and longer diabetes duration. The AI model achieved better performance in mydriatic images (accuracy, 85.15%; area under the curve, 0.94) than in non-mydriatic images (accuracy, 79.68%; area under the curve, 0.93). The McNemar test showed a significant difference in sensitivity (p=0.0001), whereas DeLong's test revealed no significant difference in area under the curve (p=0.4666). The Youden index indicated that optimal classification thresholds differed based on mydriasis status. Agreement between image fields was moderate to substantial and improved with mydriasis.
CONCLUSION: Mydriasis significantly improves image gradability and enhances AI performance in diabetic retinopathy screening. Nonetheless, in low- and middle-income countries where pharmacologic dilation may be impractical, optimizing model calibration and thresholding for non-mydriatic images is essential to ensure effective AI implementation in real-world clinical environments.

Keywords: Artificial intelligence; Bias; Diabetic retinopathy; Portable camera; Retina

Abstract

PURPOSE: To compare the refractive prediction error of Hill-radial basis function 3.0 with those of 3 conventional formulas and 11 combination methods in eyes with short axial lengths.
METHODS: The refractive prediction error was calculated using 4 formulas (Hoffer Q, SRK-T, Haigis, and Hill-RBF) and 11 combination methods (average of two or more methods). The absolute error was determined, and the proportion of eyes within 0.25-diopter (D) increments of absolute error was analyzed. Furthermore, the intraclass correlation coefficients of each method were computed to evaluate the agreement between target refractive error and postoperative spherical equivalent.
RESULTS: This study included 87 eyes. Based on the refractive prediction error findings, Hoffer Q formula exhibited the highest myopic errors, followed by SRK-T, Hill-RBF, and Haigis. Among all the methods, the Haigis and Hill-RBF combination yielded a mean refractive prediction error closest to zero. The SRK-T and Hill-RBF combination showed the lowest mean absolute error, whereas the Hoffer Q, SRK-T, and Haigis combination had the lowest median absolute error. Hill-radial basis function exhibited the highest intraclass correlation coefficient, whereas SRK-T showed the lowest. Haigis and Hill-RBF, as well as the combination of both, demonstrated the lowest proportion of refractive surprises (absolute error >1.00 D). Among the individual formulas, Hill-RBF had the highest success rate (absolute error ≤0.50 D). Moreover, among all the methods, the SRK-T and Hill-RBF combination exhibited the highest success rate.
CONCLUSIONS: Hill-radial basis function showed accuracy comparable to or surpassing that of conventional formulas in eyes with short axial lengths. The use and integration of various formulas in cataract surgery for eyes with short axial lengths may help reduce the incidence of refractive surprises.

Keywords: Cataract; Lenses, intraocular; Axial length, eye; Refractive errors; Artificial intelligence

Abstract

OBJETIVO: Nos últimos 20 anos, o número de escolas médicas no Brasil aumentou, mas as vagas para especialização em Oftalmologia não acompanharam a demanda crescente. Este estudo quer estimar a demanda por especialização e avaliar a oferta de oportunidades de aprendizado em Oftalmologia.
MÉTODOS: Estudo epidemiológico com pesquisa em banco de dados provenientes do Ministério da Educação e Conselho Brasileiro de Oftalmologia. Estes dados foram checados através de 120 editais publicados pelos serviços de Residência em 2021.
RESULTADOS: De 2002 a 2021, o número de vagas em faculdades de Medicina aumentou 370%, enquanto o número de vagas certificadas de especialização em Oftalmologia aumentou 64%. Houve um desalinhamento de 11.4% entre os dados do Conselho Brasileiro de Oftalmologia e do Ministério da Educação.
CONCLUSÃO: A proporção de graduados em Medicina aumentou muito mais do que a oferta de oportunidades de especialização em Oftalmologia, o impacto disto na busca por vagas de especialização não acreditadas é desconhecido, políticas de monitoramento das vagas de especialização em Oftalmologia devem ser estabelecidas.

Keywords: Oftalmologia; Ensino; Educação médica; Especialização

Abstract

PURPOSE: Standard automated perimetry has been the standard method for measuring visual field changes for several years. It can measure an individual’s ability to detect a light stimulus from a uniformly illuminated background. In the management of glaucoma, the primary objective of perimetry is the identification and quantification of visual field abnormalities. It also serves as a longitudinal evaluation for the detection of disease progression. The development of artificial intelligence-based models capable of interpreting tests could combine technological development with improved access to healthcare.
METHODS: In this observational, cross-sectional, descriptive study, we used an artificial intelligence-based model [Inception V3] to interpret gray-scale crops from standard automated perimetry that were performed in an ophthalmology clinic in the Brazilian Amazon rainforest between January 2018 and December 2022.
RESULTS: The study included 1,519 standard automated perimetry test results that were performed using Humphrey HFA-II-i-750 (Zeiss Meditech). The Subsequently, 70%, 10%, and 20% of the dataset were used for training, validation, and testing, respectively. The model achieved 80% (68.23%–88.9%) sensitivity and 94.64% (88.8%–98%) specificity for detecting altered perimetry results. Furthermore, the area under the receiver operating characteristic curve was 0.93.
CONCLUSIONS: The integration of artificial intelligence in the diagnosis, screening, and monitoring of pathologies represents a paradigm shift in ophthalmology, enabling significant improvements in safety, efficiency, availability, and accessibility of treatment.

Keywords: Glaucoma; Disease progression; Perimetry; Visual Fields; Visual field tests; Artificial intelligence; Neural networks, computers; Machine learning

Abstract

PURPOSE: To determine and analyze the usability metrics of a free mobile learning app for ophthalmology in Brazil.
METHODS: Metric data from the management dashboard of the CBOQUIZ app were used. All users registered on the platform between March 2019 and June 30, 2021 were included. The number of questions answered, number of correct answers, number of questions answered and correct answers by subject area, and user performance by geographic region were analyzed.
RESULTS: There were 458 active users during the research period and 107,245 questions answered (average, 234.16 questions per user). Of the questions answered, 81,600 (75.5%) were correct and 2,645 were incorrect. The states in Brazil with the best performance were Espírito Santo, Paraiba, and Paraná. The subject area with the lowest hit rate was basic sciences (69.1%), within which embryology demonstrated the lowest hit rate (58.28%). The posterior segment had the highest number of questions answered, followed by miscellaneous topics and the anterior segment. Questions on strabismus were the least answered.
CONCLUSION: The app was used consistently throughout the period studied, and participants adhered to this teaching modality. Performance asymmetry was observed across the Brazil states. The CBOQUIZ app can be used to homogenize ophthalmology teaching in the country.

Keywords: Ophthalmology; Mobile applications; Teaching; Smartphone; Education, distance; Internship and residency; Brazil

Abstract

PURPOSE: The OrbiTau surgical simulator is a synthetic eye model developed to enhance cataract surgical training. Herein, we aimed to describe the perspectives of Harvard’s Ophthalmology faculty and residents regarding the effectiveness of OrbiTau.
METHODS: A cross-sectional study was conducted in which 11 surgeons from the Massachusetts Eye and Ear Infirmary, with prior experience utilizing simulated phacoemulsification platforms, conducted cataract surgery with the OrbiTau. Subsequently, they completed a satisfaction questionnaire using the Likert scale.
RESULTS: Regarding the various OrbiTau components, 90.90% of the participants reported that the OrbiTau lens capsule was comparable to that of the human lens during capsulotomy. Furthermore, 72.72% of the participants found that the OrbiTau lens consistency was analogous to that of the human lens nucleus. Approximately 63.63% of the participants reported that the model’s posterior lens capsule resembled the native posterior capsule, and 72.72% of the participants noted that the model’s red reflex was similar to that of the dilated human pupil. Most participants believed that the OrbiTau was easier to use and more realistic than other commercially available simulators.
CONCLUSION: Our single-institution survey of the Orbitau demonstrated that this model realistically replicates ocular structures and may be a viable option for cataract surgery training.

Keywords: Cataract extraction/education; Simulation training/methods; Ophthalmology/education; Phacoemulsification/education; Ophthalmologists/education; Surgeons/education; High fidelity simulation training

Abstract

PURPOSE: To assess the effect of the coronavirus disease 2019 (COVID-19) pandemic on cataract surgery by residents who had mandatory surgical simulator training during residency.
METHODS: In this retrospective, observational analytical study, the total number of cataract surgeries and surgical complications by all senior residents of 2019 (2019 class; prepandemic) and 2020 (2020 class; affected by the reduced number of elective surgeries due to the COVID-19 pandemic) were collected and compared. All residents had routine mandatory cataract surgery training on a virtual surgical simulator during residency. The total score obtained by these residents on cataract challenges of the surgical simulator was also evaluated.
RESULTS: The 2020 and 2019 classes performed 1275 and 2561 cataract surgeries, respectively. This revealed a reduction of 50.2% in the total number of procedures performed by the 2020 class because of the pandemic. The incidence of surgical complications was not statistically different between the two groups (4.2% in the 2019 class and 4.9% in the 2020 class; p=0.314). Both groups also did not differ in their mean scores on the simulator’s cataract challenges (p<0.696).
CONCLUSION: Despite the reduction of 50.2% in the total number of cataract surgeries performed by senior residents of 2020 during the COVID-19 pandemic, the incidence of surgical complications did not increase. This suggests that surgical simulator training during residency mitigated the negative effects of the reduced surgical volume during the pandemic.

Keywords: COVID-19; Pandemics; Cataract extraction/education; Internship and residency/methods; Simulation training/methods; Phacoemulsification/education; Surgery, computer-assisted; Computer simulation; Clinical competence; Ophthalmology/education